Towards Personalised Therapy in Chronic Spontaneous Urticaria: Advancing From Endotype to Clinical Response
Bibliographic record
Abstract
Chronic spontaneous urticaria (CSU) is a skin disorder characterised by recurrent hives and swellings that has a profound effect upon quality of life. Current guidelines for the management of CSU outline sequential use of standard dosing nonsedating H1 antihistamines, fourfold dose antihistamines and the anti-IgE monoclonal antibody omalizumab. A proportion of patients will have partial response or no response to omalizumab despite uptitration of dose. Evidence suggests that nonresponders may represent a specific endotype of Type IIb autoimmune CSU and respond better to ciclosporin, a fourth line off-licence treatment. Accurate and timely classification of CSU by endotype may enable personalised medicine for patients. Current attempts to classify CSU are based on distinct autoallergic and autoimmune pathways towards mast cell activation; Type I autoallergic urticaria as evidenced by IgE autoantibodies initiating FcεR1 crosslinking and Type IIb autoimmune urticaria as evidenced by IgG autoantibodies initiating FcεR1 crosslinking. However, recent data have demonstrated that the distinction between CSU endotypes is more nuanced, with overlap between categories whereby patients with Type IIb autoimmune CSU have been found to have coexistent IgE autoantibodies. A cohort of patients do not meet criteria for either endotype. Furthermore, there is recognition that laboratory parameters currently used to stratify patients are not widely available, hampering their practical use. This review seeks to summarise data on biomarkers associated with treatment response in CSU. While previous literature has focussed upon treatment response to antihistamines, our emphasis is on predicting treatment response to third and fourth-line treatments, with further reference to emerging treatments that do not yet form part of guidelines for management of CSU. Understanding factors that influence clinical response to all agents is particularly important as treatment options for CSU rapidly expand. In the following sections, we will evaluate the biochemical and clinical parameters that have been explored in these patients as well as their potential utility in routine clinical practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".